In stage-2, we use the best generator of stage-1 to provide supervision signals for searching the Pareto-front of discriminators, from which we further select the most proper discriminators for the CIFAR-10 and STL-10 datasets, respectively. Similar to the searched generator, the searched architectures of discriminators (see Appendix) also have many “None” and “Skip-connect” operations due to the objective of model size. From Tab. REF , we can see that the existing NAS-GAN methods cannot guarantee to find excellent GANs under both two search scenarios: (a) searching only generators and (b) searching both generators and discriminators. For example, AdverearialNAS [1]}
does not perform well in scenario (a), and AlphaGAN [2]} suffers from instability in scenario (b) as its performance drops significantly from (IS=8.89\(\pm \) 0.09, FID=10.35) to (IS=8.70\(\pm \) 0.11, FID=15.56) when searching generators and discriminators. Our EAGAN can well balance these two search settings. The results show that the searched discriminator can further facilitate the performance of the generator discovered in stage-1. Specifically, we achieve a higher IS value (8.81\(\pm \) 0.10) and the best FID (9.91) on the CIFAR-10 dataset. Besides, our EAGAN achieves remarkable results (IS=10.44\(\pm \) 0.08, FID=22.18) on the STL-10 dataset, which outperforms all other NAS-searched GANs. In Appendix Fig. 1, we randomly present 50 images generated by generators trained on the CIFAR-10 and the STL-10 dataset without cherry-picking, respectively. The generated images are of rich diversity and high quality.
